CN111639682A - Ground segmentation method and device based on point cloud data - Google Patents
Ground segmentation method and device based on point cloud data Download PDFInfo
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Abstract
The specification discloses a ground segmentation method and a ground segmentation device based on point cloud data, which can acquire the point cloud data, input the point cloud data into a pre-trained classification model to obtain ground type point cloud points output by the classification model, fit the point cloud points of each ground type to obtain a global plane, determine a local plane corresponding to each region according to information of the point cloud points in each region, select a plurality of regions as designated regions according to the global plane and the local planes corresponding to the regions, and determine a ground segmentation result of the point cloud data according to the information of the local planes corresponding to the designated regions. Compared with the prior art, the accuracy of the ground segmentation result of the point cloud data in the specification is higher.
Description
Technical Field
The present disclosure relates to the field of ground segmentation technologies, and in particular, to a ground segmentation method and apparatus based on point cloud data.
Background
Currently, in the field of unmanned driving technology, a laser radar is generally used by unmanned equipment to acquire point cloud data, and the environmental information around the unmanned equipment can be determined by processing the point cloud data such as ground segmentation and target detection.
A common method for performing ground segmentation on point cloud data is to divide a point cloud data space into a plurality of regions, fit point cloud points in each region to obtain a region plane, and determine a normal vector of the region plane. In each area, according to a preset rule, selecting a designated area as a ground area, according to a normal vector of an area plane corresponding to each area, taking an area with an included angle with the normal vector of the area plane of the ground area smaller than a preset included angle threshold value as the ground area, according to point cloud points in each area, determining a global plane, and according to the global plane, performing ground segmentation on point cloud data.
Due to the complexity of the real scene, the ground segmentation result obtained by the method has low accuracy, and the expected effect cannot be achieved. Therefore, how to improve the accuracy of the point cloud data ground segmentation result becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the specification provides a ground segmentation method and device based on point cloud data, so as to partially solve the problems in the prior art.
The embodiment of the specification adopts the following technical scheme:
the ground segmentation method based on point cloud data provided by the specification comprises the following steps:
acquiring point cloud data;
inputting the point cloud data into a pre-trained classification model to obtain a ground type point cloud point output by the classification model;
fitting the point cloud points of each ground type to obtain a global plane; determining a local plane corresponding to each region according to the information of each cloud point in the region aiming at each region which is divided into the point cloud data in advance;
selecting a plurality of areas as designated areas according to the information of the global plane and the information of the local plane corresponding to each area;
and determining a ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated area.
Optionally, the training of the classification model in advance specifically includes:
acquiring sample point cloud data and a label corresponding to the sample point cloud data;
sampling the sample point cloud data to obtain a training sample;
inputting the training sample into a classification model to be trained to obtain the types of cloud points of each point in the training sample determined by the classification model to be trained;
and training the classification model to be trained according to the obtained types of the cloud points of each point in the training sample and the labels.
Optionally, inputting the point cloud data into a classification model trained in advance, specifically including:
and sampling the point cloud data, and inputting the sampled point cloud data into the classification model.
Optionally, selecting a plurality of regions as the designated regions according to the information of the global plane and the information of the local plane corresponding to each region, specifically including:
for each area, if the number of the point cloud points of the ground type in the area is greater than a preset number threshold, and an included angle between the global plane and a local plane corresponding to the area is smaller than a preset included angle threshold, taking the area as a reference area;
if the number of the ground-type point cloud points in the area is not larger than the number threshold, when the area is adjacent to the determined reference area or the determined expansion area and the included angle between the global plane and the local plane corresponding to the area is smaller than the included angle threshold, taking the area as the expansion area;
and taking the reference region and the extension region as the designated region.
Optionally, when the number of the ground-type point cloud points in the area is greater than a preset number threshold, determining a local plane corresponding to the area according to information of the point cloud points in the area, specifically including:
and fitting the point cloud points of each ground type in the area to obtain a local plane corresponding to the area.
Optionally, when the number of ground-type cloud points in the area is not greater than the number threshold and the area is adjacent to the determined reference area or the determined extended area, determining a local plane corresponding to the area according to information of cloud points in the area, specifically including:
point cloud points of each ground type in a reference area or an expansion area adjacent to the area are used as reference points;
and fitting each reference point and each cloud point in the area to obtain a local plane corresponding to the area.
Optionally, determining a ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated area, specifically including:
aiming at each point cloud point in the specified area, determining the distance between the point cloud point and the local plane corresponding to the specified area according to the information of the point cloud point and the information of the local plane corresponding to the specified area, and taking the distance as the ground clearance of the point cloud point;
and determining a ground segmentation result of the point cloud data according to the ground clearance of each point cloud point in the designated area and a preset ground clearance threshold.
The present specification provides a ground segmentation apparatus based on point cloud data, the apparatus comprising:
the acquisition module is used for acquiring point cloud data;
the input module is used for inputting the point cloud data into a pre-trained classification model to obtain a ground type point cloud point output by the classification model;
the fitting module is used for fitting the point cloud points of each ground type to obtain a global plane; determining a local plane corresponding to each region according to the information of each cloud point in the region aiming at each region which is divided into the point cloud data in advance;
the selection module is used for selecting a plurality of areas as designated areas according to the information of the global plane and the information of the local plane corresponding to each area;
and the determining module is used for determining the ground segmentation result of the point cloud data according to the information of the local plane corresponding to the specified area.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described ground segmentation method based on point cloud data.
The electronic device provided by the present specification includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the processor implements the above ground segmentation method based on point cloud data.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
the method comprises the steps of firstly obtaining point cloud data, inputting the point cloud data into a pre-trained classification model to obtain point cloud points of ground types output by the classification model, fitting the point cloud points of all the ground types to obtain a global plane, aiming at each region divided by the point cloud data in advance, determining a local plane corresponding to the region according to information of the point cloud points in the region, selecting a plurality of regions as designated regions according to the global plane and the local plane corresponding to the regions, and determining a ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated regions. The method comprises the steps of respectively fitting a global plane and each local plane on the basis of obtaining point cloud points of ground types through a classification model, determining a designated area, and determining a ground segmentation result of point cloud data according to information of the local plane corresponding to the designated area.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
fig. 1 is a flowchart of a ground segmentation method based on point cloud data according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of regions partitioned from point cloud data according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of a method for determining a designated area according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of a method for pre-training a classification model provided herein;
fig. 5 is a schematic structural diagram of a ground segmentation apparatus based on point cloud data according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of an electronic device corresponding to fig. 1 provided in an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a ground segmentation method based on point cloud data according to an embodiment of the present disclosure, which may specifically include the following steps:
s100: and acquiring point cloud data.
In this specification, the point cloud data may be acquired by a lidar. In the point cloud data, the information of the point cloud point may include spatial position information, laser reflection intensity information, color information, or the like. When the laser radar is placed on the unmanned equipment, the unmanned equipment can determine information in the surrounding environment of the unmanned equipment through processing point cloud data, and therefore control over the unmanned equipment is achieved. The unmanned equipment mainly comprises intelligent unmanned equipment such as unmanned vehicles and unmanned aerial vehicles, and is mainly used for replacing manual goods delivery, for example, goods after being sorted are transported in a large goods storage center, or the goods are transported to another place from a certain place. The processing of the point cloud data may include ground segmentation, target detection, and the like, and by performing the ground segmentation on the point cloud data, the unmanned device may determine ground information in an environment around the unmanned device. Of course, the laser radar can also be placed on a common vehicle to acquire point cloud data.
In this specification, the execution subject for performing ground segmentation on point cloud data may be a dedicated processing device, may also be an unmanned device, and may also be another device capable of processing point cloud data.
The data processing equipment can acquire the point cloud data acquired by the laser radar in real time and also can acquire the point cloud data acquired by the laser radar in history. This is not limited by the present description.
S102: and inputting the point cloud data into a pre-trained classification model to obtain the ground type point cloud points output by the classification model.
After the point cloud data is obtained, the data processing equipment can input the point cloud data into a pre-trained classification model to obtain the types of cloud points of each point output by the classification model. The type of the point cloud point can be set according to actual requirements, for example, the ground type and the non-ground type, and the non-ground type can further include a suspension subtype and other subtypes. The ground-type point cloud points refer to point cloud points representing the ground in the point cloud data, that is, point cloud points acquired by laser radar emitting laser to the ground through ground reflection, and non-ground-type point cloud points refer to point cloud points not representing the ground in the point cloud data.
Specifically, first, the data processing device may sample point cloud data, and input the sampled point cloud data into the classification model. The point cloud data can be randomly sampled to obtain a fixed number of point cloud points because the different point cloud data contain different numbers of point cloud points, so that the point cloud data can be processed by the classification model conveniently. Of course, when the classification model is trained in advance, the training samples also need to be sampled to obtain a fixed number of point cloud points. Among them, details about the content of training the classification model in advance will be described in detail below.
Then, the data processing apparatus may obtain the types of cloud points of each point in the sampled point cloud data output by the classification model. In the output result of the classification model, the data processing device may select a face type of point cloud points.
Specifically, the classification model can output confidence degrees of types of cloud points of each point in the sampled point cloud data, and determine the types of the cloud points according to the confidence degrees. Of course, the classification model can also directly output the types of the point cloud points.
The classification model can comprise a PointNet model, a PointNet + + model and a VoxelNet model, of course, the classification model can also be other machine learning models capable of realizing ground segmentation of point cloud data, and when the classification model is other machine learning models, the point cloud data can be directly input into other machine learning models according to information such as model parameters of other machine learning models without sampling the point cloud data.
S104: fitting the point cloud points of each ground type to obtain a global plane; and aiming at each region which is divided into the point cloud data in advance, determining a local plane corresponding to the region according to the information of each point cloud point in the region.
S106: and selecting a plurality of areas as the designated areas according to the information of the global plane and the information of the local plane corresponding to each area.
After the point cloud points of each ground type in the sampled point cloud data are obtained, the data processing device can fit the point cloud points of each ground type in the sampled point cloud data according to the information of the point cloud points of each ground type in the sampled point cloud data to obtain a global plane. The global plane may be regarded as a ground surface obtained by fitting point cloud points representing the ground surface in the point cloud data, and certainly, the global plane is not a final result of ground segmentation of the point cloud data in this specification, and the global plane may be used as a reference to obtain a more accurate ground segmentation result of the point cloud data.
The process of obtaining the global plane by fitting the point cloud points of each ground type according to the information of the point cloud points of each ground type in the sampled point cloud data can be determined by a ransac (random Sample consensus) algorithm in the prior art, can also be determined by other algorithms improved based on a least square method, and can certainly be determined by other fitting algorithms. The process of obtaining the plane by fitting the cloud point of the point in the prior art is not described in detail herein.
The data processing apparatus may also divide the areas of the point cloud data in advance.
Specifically, the information of the point cloud points in the point cloud data may include spatial position information, that is, in a three-dimensional coordinate system, may be represented as (x, y, z), and the description mainly performs ground segmentation processing on the point cloud data, so that a top view of a space where the point cloud data is located may be divided into a plurality of sub-planes, that is, the (x, y) plane is divided into a plurality of sub-planes, and the space where the point cloud data is located is divided into a plurality of sub-spaces, each of which is an area. And regarding each region, taking the point cloud point with the spatial position in the region as the point cloud point in the region according to the spatial position information of the point cloud point and the spatial position information of the region.
After dividing the plurality of areas, the data processing apparatus may select at least one area as the designated area among the areas.
And for each area, if the number of the point cloud points of the ground type in the area is greater than a preset number threshold, and an included angle between the global plane and a local plane corresponding to the area is smaller than a preset included angle threshold, taking the area as a reference area.
Specifically, for each area, according to the types of cloud points of each point in the area, whether the number of point cloud points of the ground type in the area is greater than a preset number threshold is judged, if so, the point cloud points of each ground type in the area can be fitted according to the types of cloud points of each point in the area to obtain a local plane corresponding to the area, whether an included angle between the global plane and the local plane corresponding to the area is smaller than a preset included angle threshold is judged, and if the judgment result is smaller, the area can be used as a reference area.
The data processing equipment can fit point cloud points of various ground types in the reference area to obtain a local plane corresponding to the reference area, and can fit the point cloud points of various ground types in the reference area to obtain a specific process of the local plane corresponding to the reference area, and can refer to the process of obtaining a global plane by referring to the point cloud points of various ground types in the point cloud data after fitting and sampling.
And if the number of the point cloud points of the ground type in the area is not more than the number threshold, when the area is adjacent to the determined reference area or the determined expansion area and the included angle between the global plane and the local plane corresponding to the area is less than the included angle threshold, taking the area as the expansion area.
Specifically, when the number of the ground-type point cloud points in the area is judged to be not more than a preset number threshold, whether an adjacent area of the area is a determined reference area or an expansion area can be judged, and if the judgment result is yes, the data processing device can take the ground-type point cloud points in the adjacent reference area or the expansion area of the area as reference points, fit the reference points and the cloud points of each point in the area, and obtain a local plane corresponding to the area. Then, whether the included angle between the global plane and the local plane corresponding to the area is smaller than an included angle threshold value is judged, and if the judged result is smaller than the included angle threshold value, the area can be used as an expansion area.
Finally, the data processing apparatus may take the reference region and the extended region as the designated regions. When an area is determined as a reference area, firstly, the number of the ground-type point cloud points in the area is determined to be sufficient, and then, the included angle between the local plane corresponding to the area and the global plane, which is obtained by fitting the ground-type point cloud points in the area, is determined to be sufficiently small, so that the point cloud points in the area can be determined to be represented as the ground, that is, the area is a designated area. When an area is determined to be an expanded area, although the number of ground-type point cloud points in the area is small, when an adjacent area of the area is a determined reference area or an already determined expanded area, that is, point cloud points of adjacent areas of the area can be represented as the ground, the probability that the point cloud points in the area are represented as the ground is high, so that local planes corresponding to the area can be obtained by fitting the point cloud points of each ground type of the reference area or the expanded area adjacent to the area and the point cloud points in the area, and when an included angle between a global plane and the local plane corresponding to the area is determined to be small enough, the area can be regarded as the expanded area, and the point cloud points in the area can be represented as the ground, that is, the area is a designated area.
In addition, before judging whether the included angle between the global plane and the local plane corresponding to the area is smaller than the included angle threshold, the data processing device may determine the included angle between the global plane and the local plane corresponding to the area. Specifically, the data processing device may determine a normal vector of the global plane according to the information of the global plane, determine a normal vector of the local plane corresponding to the region according to the information of the local plane corresponding to the region, and use an included angle between the normal vector of the global plane and the normal vector of the local plane corresponding to the region as an included angle between the global plane and the local plane corresponding to the region.
Of course, the data processing device may also determine the included angle between the global plane and the local plane corresponding to the region by other methods, for example, the data processing device may determine an intersection line between the global plane and the local plane corresponding to the region, and may determine the included angle between the global plane and the local plane corresponding to the region according to the point cloud points of each ground type of the global plane obtained by fitting and the point cloud points of each local plane corresponding to the region obtained by fitting.
Fig. 2 is a schematic diagram of regions into which point cloud data is divided according to an embodiment of the present specification. In fig. 2, a vehicle a is a vehicle on which a laser radar is placed, vehicles B and C are other vehicles (with respect to the vehicle a, the vehicles B and C are obstacles), each square is an area, a circular point is a ground-type point cloud point output by a classification model, a triangular point is a non-ground-type point cloud point output by the classification model, each point cloud point in each square is a point cloud point in the area, a gray-filled square is a reference area, a grid-filled square is an expanded area, and a non-filled square is not a designated area. The global plane and the local plane corresponding to each region are not shown in fig. 2.
Fig. 3 is a flowchart of a method for determining a designated area according to an embodiment of the present disclosure, which may specifically include the following steps:
s300: and selecting the area with the number of the ground type point cloud points larger than a preset number threshold value as the undetermined area.
Specifically, for each region, the data processing device may determine, according to the type of each point cloud point in the region, the number of point cloud points of a ground type in the region, determine whether the number of point cloud points of the ground type in the region is greater than a number threshold, if so, determine the region as pending, and otherwise, determine that the region is not a pending region.
S302: and fitting the point cloud points of each ground type in the to-be-determined area to obtain a local plane corresponding to the to-be-determined area.
S304: and judging whether the included angle between the global plane and the local plane corresponding to the undetermined area is smaller than a preset included angle threshold value, if so, executing the step S306, otherwise, executing the step S308.
S306: and determining the undetermined area as the designated area.
S308: determining that the pending region is not the designated region.
Specifically, when the undetermined area is a designated area, that is, the number of point cloud points of the ground type in the undetermined area is greater than the number threshold, and the included angle between the global plane and the local plane corresponding to the undetermined area is smaller than the included angle threshold, the undetermined area is the reference area in the foregoing text, and in fig. 2, the undetermined area is represented as a gray-filled square.
S310: and taking the area adjacent to the designated area as a candidate area.
Specifically, according to the above steps S300 to S308, after all the designated areas are selected from the areas, the data processing apparatus may set the area adjacent to the designated area as the candidate area. At this time, the candidate area may include the non-filled squares in fig. 2 and the squares filled with the grid.
S312: and fitting the point cloud points of each ground type in the designated area and the point cloud points of the candidate area to obtain a local plane corresponding to the candidate area.
Specifically, for each candidate region, the data processing device may fit point cloud points of each ground type in a designated region adjacent to the candidate region and each point cloud point in the candidate region to obtain a local plane corresponding to the candidate region.
S314: and judging whether the included angle between the global plane and the local plane corresponding to the candidate area is smaller than a preset included angle threshold value, if so, executing a step S316, otherwise, executing a step S318.
S316: and taking the candidate area as the designated area.
S318: determining that the candidate region is not the designated region.
Specifically, when the candidate region is a designated region, that is, the number of point cloud points of the ground type in the candidate region is smaller than a number threshold, and an included angle between the global plane and a local plane corresponding to the candidate region is smaller than an included angle threshold, the candidate region is the above extended region. At this time, the candidate region is a square of the filling grid in fig. 2.
When the candidate region is not the designated region, that is, the number of the point cloud points of the ground type in the candidate region is smaller than the number threshold, but an included angle between the global plane and the local plane corresponding to the candidate region is not smaller than the included angle threshold, at this time, the candidate region is a square grid without filling in fig. 2.
After the data processing apparatus takes the candidate region as the designated region, the data processing apparatus may regard a region adjacent to the candidate region as the candidate region again (excluding the already determined pending region and candidate region), and execute steps S310 to S318 until the designated region cannot be determined again.
S108: and determining a ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated area.
After selecting the designated areas, for each designated area, the data processing apparatus may determine a ground segmentation result of the point cloud data in the designated area.
Specifically, for each cloud point in the designated area, the data processing device may first determine, according to the information of the cloud point and the information of the local plane corresponding to the designated area, a distance between the cloud point and the local plane corresponding to the designated area as a ground clearance of the cloud point, and then determine, according to the ground clearance of each cloud point in the designated area and a preset ground clearance threshold, a ground segmentation result of the cloud point data in the designated area.
For point cloud points of the acquired point cloud data (i.e., the point cloud data before sampling) in the specified area, the data processing device may determine the ground clearance of the point cloud point, determine whether the ground clearance of the point cloud point is smaller than a threshold of the ground clearance, if the determination result is smaller than the threshold, the point cloud point may be used as a ground-type point cloud point, and if the determination result is not smaller than the threshold, the point cloud point may be used as a non-ground-type point cloud point.
According to the information of the point cloud points of the ground type in each designated area, the data processing device can determine the ground segmentation result of the acquired point cloud data.
The process of determining the distance between the point cloud point and the local plane corresponding to the designated area can be determined by the prior art, and is not described herein again.
In this specification, the data processing apparatus may also train the classification model in advance, and fig. 4 is a flowchart of a method for training the classification model in advance, which may specifically include the following steps:
s400: and acquiring sample point cloud data and a label corresponding to the sample point cloud data.
Specifically, the data processing device may obtain sample point cloud data historically collected by the laser radar, and obtain a ground segmentation result of the sample point cloud data according to a method provided in the prior art, that is, obtain types of cloud points of each point in the sample point cloud data, because the types of cloud points of each point obtained in the prior art are not accurate enough, on the basis of obtaining the types of cloud points of each point in the sample point cloud data in the prior art, determine the types of cloud points of each point in the sample point cloud data by means of manual screening and manual labeling, that is, mark the sample point cloud data, wherein the label includes information such as the types of the cloud points of the point, and the types of the cloud points of the point may include ground types, non-ground types, and the like.
S402: and sampling the sample point cloud data to obtain a training sample.
Specifically, the data processing device may randomly sample the sample point cloud data, and use the sampled sample point cloud data as a training sample.
S404: and inputting the training sample into a classification model to be trained to obtain the types of cloud points of each point in the training sample determined by the classification model to be trained.
Specifically, the training sample is input into the classification model to be trained, the type of each cloud point output by the classification model to be trained can be obtained, the classification model to be trained can output the confidence coefficient of the type of each cloud point in the sampled point cloud data, and the type of the cloud point is determined according to the confidence coefficient.
S406: and training the classification model to be trained according to the obtained types of the cloud points of each point in the training sample and the labels.
After the confidence coefficient and the type of each cloud point are obtained through the classification model to be trained, the loss can be determined according to the type and the label of each cloud point in the training sample, and the classification model to be trained is trained by taking the minimized loss as a training target.
Wherein, the loss can be cross entropy loss or Focal point loss (Focal loss), and when the loss is Focal loss, the parameter in the loss can be determined through experiments.
The ground segmentation method based on point cloud data provided by the specification can be particularly applied to the field of distribution by using unmanned equipment, for example, the distribution scene of express delivery, takeaway and the like by using the unmanned equipment. Specifically, in the above-described scenario, delivery may be performed using an unmanned vehicle fleet configured with a plurality of unmanned devices.
Based on the point cloud data-based ground segmentation method shown in fig. 1, the embodiment of the present specification further provides a schematic structural diagram of a ground segmentation apparatus based on point cloud data, as shown in fig. 5.
Fig. 5 is a schematic structural diagram of a ground segmentation apparatus based on point cloud data provided in an embodiment of the present specification, the apparatus including:
an obtaining module 501, configured to obtain point cloud data;
an input module 502, configured to input the point cloud data into a pre-trained classification model, so as to obtain a ground-type point cloud point output by the classification model;
a fitting module 503, configured to fit the point cloud points of each ground type to obtain a global plane; determining a local plane corresponding to each region according to the information of each cloud point in the region aiming at each region which is divided into the point cloud data in advance;
a selecting module 504, configured to select a plurality of regions as designated regions according to the information of the global plane and the information of the local plane corresponding to each region;
and a determining module 505, configured to determine a ground segmentation result of the point cloud data according to information of the local plane corresponding to the designated area.
Optionally, the apparatus further comprises a training module 506;
the training module 506 is specifically configured to obtain sample point cloud data and a label corresponding to the sample point cloud data; sampling the sample point cloud data to obtain a training sample; inputting the training sample into a classification model to be trained to obtain the types of cloud points of each point in the training sample determined by the classification model to be trained; and training the classification model to be trained according to the obtained types of the cloud points of each point in the training sample and the labels.
Optionally, the input module 502 is specifically configured to sample the point cloud data, and input the sampled point cloud data into the classification model.
Optionally, the selecting module 504 is specifically configured to, for each area, if the number of point cloud points of the ground type in the area is greater than a preset number threshold, and an included angle between the global plane and a local plane corresponding to the area is smaller than a preset included angle threshold, take the area as a reference area; if the number of the ground-type point cloud points in the area is not larger than the number threshold, when the area is adjacent to the determined reference area or the determined expansion area and the included angle between the global plane and the local plane corresponding to the area is smaller than the included angle threshold, taking the area as the expansion area; and taking the reference region and the extension region as the designated region.
Optionally, when the number of the ground-type point cloud points in the area is greater than a preset number threshold, the fitting module 503 is specifically configured to fit the ground-type point cloud points in the area to obtain a local plane corresponding to the area.
Optionally, when the number of the ground-type point cloud points in the area is not greater than the number threshold and the area is adjacent to the determined reference area or the determined expansion area, the fitting module 503 is specifically configured to use each ground-type point cloud point in the reference area or the expansion area adjacent to the area as a reference point; and fitting each reference point and each cloud point in the area to obtain a local plane corresponding to the area.
Optionally, the determining module 505 is specifically configured to, for each cloud point in the designated area, determine, according to information of the cloud point and information of a local plane corresponding to the designated area, a distance between the cloud point and the local plane corresponding to the designated area as a ground clearance of the cloud point; and determining a ground segmentation result of the point cloud data according to the ground clearance of each point cloud point in the designated area and a preset ground clearance threshold.
Embodiments of the present specification further provide a computer-readable storage medium, where the storage medium stores a computer program, and the computer program can be used to execute the above ground segmentation method based on point cloud data provided in fig. 1.
Based on the ground segmentation method based on point cloud data shown in fig. 1, the embodiment of the present specification further provides a schematic structure diagram of the electronic device shown in fig. 6. As shown in fig. 6, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the above-mentioned ground segmentation method based on point cloud data as shown in fig. 1.
Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A ground segmentation method based on point cloud data is characterized by comprising the following steps:
acquiring point cloud data;
inputting the point cloud data into a pre-trained classification model to obtain a ground type point cloud point output by the classification model;
fitting the point cloud points of each ground type to obtain a global plane; determining a local plane corresponding to each region according to the information of each cloud point in the region aiming at each region which is divided into the point cloud data in advance;
selecting a plurality of areas as designated areas according to the information of the global plane and the information of the local plane corresponding to each area;
and determining a ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated area.
2. The method of claim 1, wherein pre-training the classification model specifically comprises:
acquiring sample point cloud data and a label corresponding to the sample point cloud data;
sampling the sample point cloud data to obtain a training sample;
inputting the training sample into a classification model to be trained to obtain the types of cloud points of each point in the training sample determined by the classification model to be trained;
and training the classification model to be trained according to the obtained types of the cloud points of each point in the training sample and the labels.
3. The method of claim 1, wherein inputting the point cloud data into a pre-trained classification model comprises:
and sampling the point cloud data, and inputting the sampled point cloud data into the classification model.
4. The method according to claim 1, wherein selecting a plurality of regions as the designated regions according to the information of the global plane and the information of the local plane corresponding to each region specifically comprises:
for each area, if the number of the point cloud points of the ground type in the area is greater than a preset number threshold, and an included angle between the global plane and a local plane corresponding to the area is smaller than a preset included angle threshold, taking the area as a reference area;
if the number of the ground-type point cloud points in the area is not larger than the number threshold, when the area is adjacent to the determined reference area or the determined expansion area and the included angle between the global plane and the local plane corresponding to the area is smaller than the included angle threshold, taking the area as the expansion area;
and taking the reference region and the extension region as the designated region.
5. The method of claim 4, wherein when the number of the ground-type point cloud points in the area is greater than a preset number threshold, determining a local plane corresponding to the area according to information of the point cloud points in the area specifically comprises:
and fitting the point cloud points of each ground type in the area to obtain a local plane corresponding to the area.
6. The method according to claim 4, wherein when the number of ground-type cloud points in the area is not greater than the number threshold and the area is adjacent to the determined reference area or extended area, determining a local plane corresponding to the area according to information of cloud points in the area, specifically comprising:
point cloud points of each ground type in a reference area or an expansion area adjacent to the area are used as reference points;
and fitting each reference point and each cloud point in the area to obtain a local plane corresponding to the area.
7. The method of claim 1, wherein determining the ground segmentation result of the point cloud data according to the information of the local plane corresponding to the designated area specifically comprises:
aiming at each point cloud point in the specified area, determining the distance between the point cloud point and the local plane corresponding to the specified area according to the information of the point cloud point and the information of the local plane corresponding to the specified area, and taking the distance as the ground clearance of the point cloud point;
and determining a ground segmentation result of the point cloud data according to the ground clearance of each point cloud point in the designated area and a preset ground clearance threshold.
8. Ground segmentation device based on point cloud data, characterized in that the device comprises:
the acquisition module is used for acquiring point cloud data;
the input module is used for inputting the point cloud data into a pre-trained classification model to obtain a ground type point cloud point output by the classification model;
the fitting module is used for fitting the point cloud points of each ground type to obtain a global plane; determining a local plane corresponding to each region according to the information of each cloud point in the region aiming at each region which is divided into the point cloud data in advance;
the selection module is used for selecting a plurality of areas as designated areas according to the information of the global plane and the information of the local plane corresponding to each area;
and the determining module is used for determining the ground segmentation result of the point cloud data according to the information of the local plane corresponding to the specified area.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-7 when executing the program.
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